Artificial General Intelligence

Artificial general intelligence (AGI) is a type of artificial intelligence (AI) that matches or goes beyond human cognitive capabilities throughout a large variety of cognitive jobs. This contrasts with narrow AI, which is restricted to specific jobs. [1] Artificial superintelligence (ASI), on the other hand, refers to AGI that greatly goes beyond human cognitive capabilities. AGI is thought about one of the meanings of strong AI.

Creating AGI is a primary goal of AI research and of business such as OpenAI [2] and Meta. [3] A 2020 study recognized 72 active AGI research study and development tasks across 37 countries. [4]

The timeline for attaining AGI remains a topic of ongoing argument amongst researchers and experts. As of 2023, some argue that it might be possible in years or decades; others maintain it might take a century or longer; a minority believe it may never ever be accomplished; and another minority declares that it is already here. [5] [6] Notable AI researcher Geoffrey Hinton has actually revealed issues about the rapid progress towards AGI, recommending it could be achieved quicker than many anticipate. [7]

There is debate on the exact definition of AGI and relating to whether contemporary big language designs (LLMs) such as GPT-4 are early forms of AGI. [8] AGI is a common topic in sci-fi and futures research studies. [9] [10]

Contention exists over whether AGI represents an existential danger. [11] [12] [13] Many specialists on AI have actually stated that reducing the danger of human termination positioned by AGI needs to be a worldwide priority. [14] [15] Others discover the development of AGI to be too remote to present such a risk. [16] [17]

Terminology

AGI is also called strong AI, [18] [19] complete AI, [20] human-level AI, [5] human-level intelligent AI, or general smart action. [21]

Some scholastic sources reserve the term “strong AI” for computer system programs that experience life or consciousness. [a] In contrast, weak AI (or narrow AI) is able to fix one specific issue however lacks basic cognitive capabilities. [22] [19] Some scholastic sources use “weak AI” to refer more broadly to any programs that neither experience consciousness nor have a mind in the exact same sense as human beings. [a]

Related principles consist of artificial superintelligence and transformative AI. A synthetic superintelligence (ASI) is a hypothetical kind of AGI that is a lot more normally smart than people, [23] while the notion of transformative AI associates with AI having a big effect on society, for instance, similar to the agricultural or industrial transformation. [24]

A framework for categorizing AGI in levels was proposed in 2023 by Google DeepMind scientists. They specify five levels of AGI: emerging, proficient, professional, virtuoso, and superhuman. For instance, a competent AGI is specified as an AI that exceeds 50% of proficient grownups in a large range of non-physical jobs, and a superhuman AGI (i.e. an artificial superintelligence) is likewise specified however with a limit of 100%. They think about large language models like ChatGPT or LLaMA 2 to be instances of emerging AGI. [25]

Characteristics

Various popular meanings of intelligence have been proposed. One of the leading propositions is the Turing test. However, there are other widely known meanings, and some scientists disagree with the more popular approaches. [b]

Intelligence qualities

Researchers normally hold that intelligence is needed to do all of the following: [27]

factor, usage technique, solve puzzles, and make judgments under unpredictability
represent understanding, including typical sense understanding
plan
learn
– communicate in natural language
– if needed, integrate these skills in completion of any provided objective

Many interdisciplinary techniques (e.g. cognitive science, computational intelligence, and decision making) consider extra qualities such as imagination (the capability to form unique psychological images and ideas) [28] and autonomy. [29]

Computer-based systems that exhibit a lot of these capabilities exist (e.g. see computational imagination, automated reasoning, choice assistance system, robotic, evolutionary computation, smart representative). There is argument about whether modern-day AI systems have them to an appropriate degree.

Physical qualities

Other abilities are thought about preferable in intelligent systems, as they might affect intelligence or aid in its expression. These consist of: [30]

– the ability to sense (e.g. see, hear, and so on), and
– the capability to act (e.g. move and manipulate things, change location to explore, etc).

This includes the capability to spot and react to danger. [31]

Although the capability to sense (e.g. see, hear, etc) and the ability to act (e.g. move and control things, modification place to check out, etc) can be preferable for some smart systems, [30] these physical abilities are not strictly needed for an entity to certify as AGI-particularly under the thesis that large language models (LLMs) may already be or end up being AGI. Even from a less positive perspective on LLMs, there is no firm requirement for an AGI to have a human-like type; being a silicon-based computational system is enough, provided it can process input (language) from the external world in location of human senses. This interpretation aligns with the understanding that AGI has never been proscribed a particular physical embodiment and thus does not demand a capacity for mobility or traditional “eyes and ears”. [32]

Tests for human-level AGI

Several tests meant to validate human-level AGI have been thought about, including: [33] [34]

The idea of the test is that the machine has to try and pretend to be a man, by addressing questions put to it, and it will just pass if the pretence is reasonably convincing. A considerable portion of a jury, who must not be expert about devices, must be taken in by the pretence. [37]

AI-complete issues

A problem is informally called “AI-complete” or “AI-hard” if it is thought that in order to fix it, one would need to carry out AGI, because the option is beyond the abilities of a purpose-specific algorithm. [47]

There are numerous problems that have actually been conjectured to need basic intelligence to fix as well as human beings. Examples consist of computer system vision, natural language understanding, and handling unexpected scenarios while solving any real-world problem. [48] Even a particular job like translation requires a machine to check out and compose in both languages, follow the author’s argument (reason), comprehend the context (knowledge), and faithfully replicate the author’s initial intent (social intelligence). All of these problems need to be fixed concurrently in order to reach human-level device performance.

However, much of these jobs can now be carried out by contemporary large language models. According to Stanford University’s 2024 AI index, AI has reached human-level efficiency on many benchmarks for reading understanding and visual reasoning. [49]

History

Classical AI

Modern AI research study started in the mid-1950s. [50] The very first generation of AI researchers were persuaded that artificial basic intelligence was possible and that it would exist in simply a couple of years. [51] AI pioneer Herbert A. Simon wrote in 1965: “machines will be capable, within twenty years, of doing any work a man can do.” [52]

Their predictions were the motivation for Stanley Kubrick and Arthur C. Clarke’s character HAL 9000, who embodied what AI researchers thought they might create by the year 2001. AI pioneer Marvin Minsky was an expert [53] on the project of making HAL 9000 as realistic as possible according to the consensus forecasts of the time. He said in 1967, “Within a generation … the problem of creating ‘artificial intelligence’ will significantly be solved”. [54]

Several classical AI projects, such as Doug Lenat’s Cyc project (that began in 1984), and Allen Newell’s Soar project, were directed at AGI.

However, in the early 1970s, it became apparent that scientists had grossly undervalued the difficulty of the project. Funding companies became hesitant of AGI and put scientists under increasing pressure to produce helpful “applied AI“. [c] In the early 1980s, Japan’s Fifth Generation Computer Project revived interest in AGI, setting out a ten-year timeline that included AGI objectives like “carry on a table talk”. [58] In reaction to this and the success of specialist systems, both industry and federal government pumped cash into the field. [56] [59] However, self-confidence in AI stunningly collapsed in the late 1980s, and the objectives of the Fifth Generation Computer Project were never ever satisfied. [60] For the second time in twenty years, AI scientists who the impending achievement of AGI had actually been mistaken. By the 1990s, AI researchers had a reputation for making vain promises. They became reluctant to make predictions at all [d] and prevented mention of “human level” synthetic intelligence for worry of being labeled “wild-eyed dreamer [s]. [62]

Narrow AI research

In the 1990s and early 21st century, mainstream AI achieved commercial success and academic respectability by concentrating on particular sub-problems where AI can produce proven results and commercial applications, such as speech recognition and suggestion algorithms. [63] These “applied AI” systems are now utilized extensively throughout the innovation market, and research in this vein is greatly funded in both academic community and market. Since 2018 [upgrade], development in this field was considered an emerging trend, and a mature phase was expected to be reached in more than 10 years. [64]

At the turn of the century, lots of traditional AI scientists [65] hoped that strong AI could be established by integrating programs that solve various sub-problems. Hans Moravec composed in 1988:

I am confident that this bottom-up route to artificial intelligence will one day satisfy the conventional top-down path more than half method, prepared to supply the real-world skills and the commonsense knowledge that has been so frustratingly elusive in thinking programs. Fully intelligent makers will result when the metaphorical golden spike is driven joining the 2 efforts. [65]

However, even at the time, this was disputed. For example, Stevan Harnad of Princeton University concluded his 1990 paper on the sign grounding hypothesis by specifying:

The expectation has typically been voiced that “top-down” (symbolic) approaches to modeling cognition will in some way satisfy “bottom-up” (sensory) approaches somewhere in between. If the grounding factors to consider in this paper stand, then this expectation is hopelessly modular and there is really just one practical path from sense to symbols: from the ground up. A free-floating symbolic level like the software level of a computer will never ever be reached by this path (or vice versa) – nor is it clear why we should even try to reach such a level, given that it looks as if arriving would just amount to uprooting our symbols from their intrinsic significances (thus merely decreasing ourselves to the practical equivalent of a programmable computer). [66]

Modern synthetic general intelligence research

The term “artificial basic intelligence” was utilized as early as 1997, by Mark Gubrud [67] in a conversation of the ramifications of fully automated military production and operations. A mathematical formalism of AGI was proposed by Marcus Hutter in 2000. Named AIXI, the proposed AGI agent increases “the ability to satisfy goals in a large variety of environments”. [68] This kind of AGI, identified by the ability to maximise a mathematical definition of intelligence rather than display human-like behaviour, [69] was also called universal expert system. [70]

The term AGI was re-introduced and popularized by Shane Legg and Ben Goertzel around 2002. [71] AGI research activity in 2006 was explained by Pei Wang and Ben Goertzel [72] as “producing publications and initial results”. The very first summertime school in AGI was organized in Xiamen, China in 2009 [73] by the Xiamen university’s Artificial Brain Laboratory and OpenCog. The very first university course was given up 2010 [74] and 2011 [75] at Plovdiv University, Bulgaria by Todor Arnaudov. MIT provided a course on AGI in 2018, organized by Lex Fridman and featuring a variety of visitor lecturers.

Since 2023 [upgrade], a small number of computer researchers are active in AGI research study, and numerous add to a series of AGI conferences. However, significantly more researchers are interested in open-ended learning, [76] [77] which is the idea of enabling AI to constantly find out and innovate like people do.

Feasibility

As of 2023, the advancement and potential accomplishment of AGI remains a topic of extreme debate within the AI community. While traditional consensus held that AGI was a far-off objective, current developments have actually led some researchers and market figures to claim that early kinds of AGI may currently exist. [78] AI pioneer Herbert A. Simon hypothesized in 1965 that “devices will be capable, within twenty years, of doing any work a guy can do”. This forecast failed to come true. Microsoft co-founder Paul Allen believed that such intelligence is not likely in the 21st century since it would require “unforeseeable and essentially unpredictable developments” and a “clinically deep understanding of cognition”. [79] Writing in The Guardian, roboticist Alan Winfield claimed the gulf between contemporary computing and human-level expert system is as wide as the gulf in between existing area flight and practical faster-than-light spaceflight. [80]

A more difficulty is the absence of clearness in defining what intelligence involves. Does it require awareness? Must it show the ability to set goals along with pursue them? Is it purely a matter of scale such that if model sizes increase adequately, intelligence will emerge? Are facilities such as planning, reasoning, and causal understanding required? Does intelligence require clearly duplicating the brain and its specific professors? Does it require feelings? [81]

Most AI scientists think strong AI can be accomplished in the future, but some thinkers, like Hubert Dreyfus and Roger Penrose, deny the possibility of achieving strong AI. [82] [83] John McCarthy is amongst those who think human-level AI will be achieved, but that today level of development is such that a date can not properly be anticipated. [84] AI professionals’ views on the expediency of AGI wax and wane. Four polls conducted in 2012 and 2013 recommended that the median estimate amongst professionals for when they would be 50% confident AGI would get here was 2040 to 2050, depending on the poll, with the mean being 2081. Of the specialists, 16.5% answered with “never ever” when asked the same question however with a 90% self-confidence instead. [85] [86] Further existing AGI progress factors to consider can be discovered above Tests for confirming human-level AGI.

A report by Stuart Armstrong and Kaj Sotala of the Machine Intelligence Research Institute discovered that “over [a] 60-year time frame there is a strong predisposition towards anticipating the arrival of human-level AI as between 15 and 25 years from the time the forecast was made”. They analyzed 95 forecasts made in between 1950 and 2012 on when human-level AI will happen. [87]

In 2023, Microsoft scientists released a detailed examination of GPT-4. They concluded: “Given the breadth and depth of GPT-4’s abilities, we think that it might reasonably be viewed as an early (yet still incomplete) variation of a synthetic basic intelligence (AGI) system.” [88] Another research study in 2023 reported that GPT-4 outperforms 99% of human beings on the Torrance tests of imaginative thinking. [89] [90]

Blaise Agüera y Arcas and Peter Norvig wrote in 2023 that a significant level of basic intelligence has already been accomplished with frontier designs. They composed that unwillingness to this view originates from four primary reasons: a “healthy skepticism about metrics for AGI”, an “ideological commitment to alternative AI theories or techniques”, a “dedication to human (or biological) exceptionalism”, or a “issue about the financial ramifications of AGI”. [91]

2023 likewise marked the development of big multimodal designs (large language models efficient in processing or producing multiple methods such as text, audio, and images). [92]

In 2024, OpenAI launched o1-preview, the first of a series of models that “invest more time believing before they respond”. According to Mira Murati, this ability to think before reacting represents a new, extra paradigm. It improves design outputs by investing more computing power when generating the answer, whereas the design scaling paradigm enhances outputs by increasing the design size, training information and training compute power. [93] [94]

An OpenAI worker, Vahid Kazemi, claimed in 2024 that the business had achieved AGI, specifying, “In my viewpoint, we have already accomplished AGI and it’s even more clear with O1.” Kazemi clarified that while the AI is not yet “better than any human at any job”, it is “better than most people at the majority of tasks.” He also attended to criticisms that large language models (LLMs) merely follow predefined patterns, comparing their knowing process to the clinical approach of observing, hypothesizing, and confirming. These declarations have actually sparked debate, as they rely on a broad and unconventional definition of AGI-traditionally understood as AI that matches human intelligence throughout all domains. Critics argue that, while OpenAI’s designs demonstrate exceptional adaptability, they might not completely satisfy this standard. Notably, Kazemi’s remarks came soon after OpenAI eliminated “AGI” from the terms of its partnership with Microsoft, prompting speculation about the business’s strategic intents. [95]

Timescales

Progress in synthetic intelligence has actually traditionally gone through periods of fast progress separated by durations when development appeared to stop. [82] Ending each hiatus were essential advances in hardware, software application or both to create area for additional progress. [82] [98] [99] For example, the computer system hardware readily available in the twentieth century was not enough to carry out deep learning, which needs great deals of GPU-enabled CPUs. [100]

In the intro to his 2006 book, [101] Goertzel says that quotes of the time required before a really versatile AGI is built vary from ten years to over a century. As of 2007 [upgrade], the consensus in the AGI research community appeared to be that the timeline talked about by Ray Kurzweil in 2005 in The Singularity is Near [102] (i.e. between 2015 and 2045) was possible. [103] Mainstream AI researchers have given a wide variety of opinions on whether development will be this rapid. A 2012 meta-analysis of 95 such viewpoints discovered a predisposition towards forecasting that the beginning of AGI would happen within 16-26 years for modern-day and historical predictions alike. That paper has actually been criticized for how it categorized opinions as expert or non-expert. [104]

In 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton established a neural network called AlexNet, which won the ImageNet competitors with a top-5 test mistake rate of 15.3%, substantially much better than the second-best entry’s rate of 26.3% (the conventional method utilized a weighted amount of ratings from various pre-defined classifiers). [105] AlexNet was considered the preliminary ground-breaker of the present deep learning wave. [105]

In 2017, researchers Feng Liu, Yong Shi, and Ying Liu conducted intelligence tests on openly readily available and easily available weak AI such as Google AI, Apple’s Siri, and others. At the maximum, these AIs reached an IQ worth of about 47, which corresponds around to a six-year-old child in first grade. A grownup pertains to about 100 on average. Similar tests were carried out in 2014, with the IQ score reaching a maximum worth of 27. [106] [107]

In 2020, OpenAI developed GPT-3, a language model capable of carrying out lots of varied tasks without particular training. According to Gary Grossman in a VentureBeat short article, while there is consensus that GPT-3 is not an example of AGI, it is considered by some to be too advanced to be categorized as a narrow AI system. [108]

In the very same year, Jason Rohrer used his GPT-3 account to establish a chatbot, and provided a chatbot-developing platform called “Project December”. OpenAI asked for modifications to the chatbot to adhere to their security guidelines; Rohrer detached Project December from the GPT-3 API. [109]

In 2022, DeepMind developed Gato, a “general-purpose” system efficient in performing more than 600 different jobs. [110]

In 2023, Microsoft Research released a research study on an early variation of OpenAI’s GPT-4, competing that it exhibited more general intelligence than previous AI designs and showed human-level performance in tasks spanning several domains, such as mathematics, coding, and law. This research study stimulated a debate on whether GPT-4 could be considered an early, incomplete variation of synthetic basic intelligence, highlighting the need for further exploration and assessment of such systems. [111]

In 2023, the AI scientist Geoffrey Hinton mentioned that: [112]

The concept that this stuff might really get smarter than individuals – a couple of individuals believed that, […] But many people thought it was method off. And I believed it was way off. I thought it was 30 to 50 years or even longer away. Obviously, I no longer think that.

In May 2023, Demis Hassabis similarly stated that “The development in the last few years has been pretty amazing”, and that he sees no factor why it would slow down, expecting AGI within a decade and even a couple of years. [113] In March 2024, Nvidia’s CEO, Jensen Huang, stated his expectation that within five years, AI would can passing any test at least along with humans. [114] In June 2024, the AI researcher Leopold Aschenbrenner, a previous OpenAI worker, estimated AGI by 2027 to be “noticeably plausible”. [115]

Whole brain emulation

While the advancement of transformer models like in ChatGPT is considered the most appealing course to AGI, [116] [117] entire brain emulation can function as an alternative technique. With entire brain simulation, a brain design is constructed by scanning and mapping a biological brain in information, and after that copying and replicating it on a computer system or another computational device. The simulation model should be sufficiently faithful to the original, so that it acts in virtually the very same method as the initial brain. [118] Whole brain emulation is a kind of brain simulation that is gone over in computational neuroscience and neuroinformatics, and for medical research study functions. It has been talked about in artificial intelligence research [103] as an approach to strong AI. Neuroimaging technologies that could provide the necessary detailed understanding are enhancing rapidly, and futurist Ray Kurzweil in the book The Singularity Is Near [102] predicts that a map of sufficient quality will appear on a similar timescale to the computing power needed to imitate it.

Early estimates

For low-level brain simulation, a really powerful cluster of computer systems or GPUs would be required, given the massive amount of synapses within the human brain. Each of the 1011 (one hundred billion) neurons has on average 7,000 synaptic connections (synapses) to other neurons. The brain of a three-year-old kid has about 1015 synapses (1 quadrillion). This number decreases with age, stabilizing by adulthood. Estimates vary for an adult, varying from 1014 to 5 × 1014 synapses (100 to 500 trillion). [120] A price quote of the brain’s processing power, based on a basic switch design for nerve cell activity, is around 1014 (100 trillion) synaptic updates per second (SUPS). [121]

In 1997, Kurzweil looked at different quotes for the hardware required to equal the human brain and adopted a figure of 1016 computations per 2nd (cps). [e] (For contrast, if a “computation” was equivalent to one “floating-point operation” – a procedure utilized to rate existing supercomputers – then 1016 “computations” would be comparable to 10 petaFLOPS, achieved in 2011, while 1018 was achieved in 2022.) He utilized this figure to predict the needed hardware would be offered sometime between 2015 and 2025, if the rapid development in computer system power at the time of writing continued.

Current research

The Human Brain Project, an EU-funded effort active from 2013 to 2023, has actually established a particularly detailed and publicly available atlas of the human brain. [124] In 2023, scientists from Duke University carried out a high-resolution scan of a mouse brain.

Criticisms of simulation-based techniques

The synthetic nerve cell design assumed by Kurzweil and utilized in numerous current synthetic neural network implementations is basic compared to biological neurons. A brain simulation would likely have to record the in-depth cellular behaviour of biological nerve cells, presently understood just in broad summary. The overhead presented by full modeling of the biological, chemical, and physical information of neural behaviour (particularly on a molecular scale) would require computational powers a number of orders of magnitude larger than Kurzweil’s quote. In addition, the estimates do not represent glial cells, which are known to contribute in cognitive procedures. [125]

A fundamental criticism of the simulated brain method originates from embodied cognition theory which asserts that human embodiment is a necessary aspect of human intelligence and is essential to ground significance. [126] [127] If this theory is correct, any completely functional brain model will need to include more than simply the nerve cells (e.g., a robotic body). Goertzel [103] proposes virtual embodiment (like in metaverses like Second Life) as an alternative, but it is unidentified whether this would be adequate.

Philosophical point of view

“Strong AI” as defined in viewpoint

In 1980, theorist John Searle coined the term “strong AI” as part of his Chinese room argument. [128] He proposed a difference between two hypotheses about synthetic intelligence: [f]

Strong AI hypothesis: A synthetic intelligence system can have “a mind” and “consciousness”.
Weak AI hypothesis: A synthetic intelligence system can (just) imitate it believes and has a mind and consciousness.

The very first one he called “strong” because it makes a more powerful statement: it assumes something special has actually occurred to the device that goes beyond those abilities that we can test. The behaviour of a “weak AI” machine would be precisely similar to a “strong AI” machine, however the latter would also have subjective conscious experience. This use is also typical in academic AI research study and textbooks. [129]

In contrast to Searle and traditional AI, some futurists such as Ray Kurzweil use the term “strong AI” to imply “human level synthetic basic intelligence”. [102] This is not the like Searle’s strong AI, unless it is presumed that awareness is necessary for human-level AGI. Academic theorists such as Searle do not think that is the case, and to most artificial intelligence researchers the question is out-of-scope. [130]

Mainstream AI is most thinking about how a program acts. [131] According to Russell and Norvig, “as long as the program works, they do not care if you call it genuine or a simulation.” [130] If the program can behave as if it has a mind, then there is no need to understand if it actually has mind – undoubtedly, there would be no chance to inform. For AI research study, Searle’s “weak AI hypothesis” is equivalent to the statement “synthetic general intelligence is possible”. Thus, according to Russell and Norvig, “most AI researchers take the weak AI hypothesis for granted, wiki.fablabbcn.org and do not care about the strong AI hypothesis.” [130] Thus, for academic AI research study, “Strong AI” and “AGI” are 2 different things.

Consciousness

Consciousness can have various meanings, and some elements play substantial functions in sci-fi and the ethics of expert system:

Sentience (or “sensational awareness”): The capability to “feel” perceptions or feelings subjectively, as opposed to the ability to reason about understandings. Some thinkers, such as David Chalmers, utilize the term “consciousness” to refer specifically to incredible consciousness, which is roughly comparable to sentience. [132] Determining why and how subjective experience develops is known as the difficult issue of awareness. [133] Thomas Nagel described in 1974 that it “feels like” something to be mindful. If we are not mindful, then it does not feel like anything. Nagel uses the example of a bat: we can sensibly ask “what does it feel like to be a bat?” However, we are not likely to ask “what does it feel like to be a toaster?” Nagel concludes that a bat appears to be mindful (i.e., has awareness) but a toaster does not. [134] In 2022, a Google engineer claimed that the company’s AI chatbot, LaMDA, had actually accomplished life, though this claim was commonly contested by other professionals. [135]
Self-awareness: To have conscious awareness of oneself as a separate individual, especially to be purposely knowledgeable about one’s own ideas. This is opposed to merely being the “topic of one’s thought”-an operating system or debugger is able to be “knowledgeable about itself” (that is, to represent itself in the exact same method it represents everything else)-but this is not what people typically indicate when they utilize the term “self-awareness”. [g]
These qualities have an ethical measurement. AI sentience would offer rise to concerns of well-being and legal protection, similarly to animals. [136] Other elements of awareness associated to cognitive capabilities are likewise pertinent to the concept of AI rights. [137] Finding out how to incorporate innovative AI with existing legal and social structures is an emerging concern. [138]

Benefits

AGI might have a wide array of applications. If oriented towards such goals, AGI could help reduce different problems on the planet such as appetite, poverty and health problems. [139]

AGI could improve productivity and performance in most tasks. For instance, in public health, AGI might speed up medical research, notably versus cancer. [140] It could take care of the senior, [141] and democratize access to fast, top quality medical diagnostics. It might use fun, cheap and customized education. [141] The need to work to subsist might end up being outdated if the wealth produced is effectively redistributed. [141] [142] This likewise raises the question of the location of human beings in a drastically automated society.

AGI might also assist to make rational choices, and to anticipate and avoid catastrophes. It could also help to reap the advantages of potentially catastrophic technologies such as nanotechnology or climate engineering, while preventing the associated threats. [143] If an AGI’s primary objective is to prevent existential disasters such as human extinction (which might be challenging if the Vulnerable World Hypothesis ends up being true), [144] it could take measures to drastically lower the threats [143] while minimizing the effect of these measures on our quality of life.

Risks

Existential risks

AGI may represent multiple types of existential risk, which are threats that threaten “the premature extinction of Earth-originating intelligent life or the permanent and extreme destruction of its potential for desirable future advancement”. [145] The threat of human extinction from AGI has been the subject of many arguments, however there is likewise the possibility that the advancement of AGI would lead to a completely problematic future. Notably, it could be utilized to spread and protect the set of values of whoever establishes it. If humankind still has ethical blind areas similar to slavery in the past, AGI may irreversibly entrench it, avoiding moral development. [146] Furthermore, AGI might assist in mass security and brainwashing, which could be utilized to develop a steady repressive worldwide totalitarian routine. [147] [148] There is also a danger for the makers themselves. If makers that are sentient or otherwise deserving of moral consideration are mass created in the future, taking part in a civilizational course that indefinitely overlooks their well-being and interests could be an existential catastrophe. [149] [150] Considering just how much AGI could enhance mankind’s future and assistance minimize other existential threats, Toby Ord calls these existential threats “an argument for proceeding with due care”, not for “deserting AI“. [147]

Risk of loss of control and human termination

The thesis that AI presents an existential risk for humans, and that this threat requires more attention, is questionable however has been backed in 2023 by numerous public figures, AI researchers and CEOs of AI business such as Elon Musk, Bill Gates, Geoffrey Hinton, Yoshua Bengio, Demis Hassabis and Sam Altman. [151] [152]

In 2014, Stephen Hawking criticized prevalent indifference:

So, facing possible futures of enormous benefits and threats, the specialists are certainly doing everything possible to guarantee the finest result, right? Wrong. If an exceptional alien civilisation sent us a message saying, ‘We’ll show up in a few decades,’ would we just reply, ‘OK, call us when you get here-we’ll leave the lights on?’ Probably not-but this is more or less what is occurring with AI. [153]

The potential fate of humanity has actually often been compared to the fate of gorillas threatened by human activities. The contrast states that greater intelligence permitted humankind to control gorillas, which are now vulnerable in ways that they might not have expected. As a result, the gorilla has actually become a threatened species, not out of malice, however merely as a collateral damage from human activities. [154]

The skeptic Yann LeCun thinks about that AGIs will have no desire to control mankind and that we must beware not to anthropomorphize them and interpret their intents as we would for humans. He stated that people will not be “wise adequate to design super-intelligent makers, yet unbelievably stupid to the point of providing it moronic goals without any safeguards”. [155] On the other side, the principle of instrumental convergence suggests that nearly whatever their goals, intelligent agents will have factors to attempt to endure and get more power as intermediary steps to accomplishing these goals. And that this does not need having emotions. [156]

Many scholars who are worried about existential risk supporter for more research study into fixing the “control issue” to respond to the question: what kinds of safeguards, algorithms, or architectures can programmers carry out to increase the probability that their recursively-improving AI would continue to behave in a friendly, rather than destructive, manner after it reaches superintelligence? [157] [158] Solving the control issue is complicated by the AI arms race (which could result in a race to the bottom of security preventative measures in order to launch products before rivals), [159] and the usage of AI in weapon systems. [160]

The thesis that AI can position existential risk likewise has detractors. Skeptics generally say that AGI is unlikely in the short-term, or that concerns about AGI distract from other issues associated with existing AI. [161] Former Google fraud czar Shuman Ghosemajumder thinks about that for lots of people outside of the technology industry, existing chatbots and LLMs are already perceived as though they were AGI, leading to further misconception and worry. [162]

Skeptics sometimes charge that the thesis is crypto-religious, with an irrational belief in the possibility of superintelligence changing an illogical belief in a supreme God. [163] Some scientists believe that the communication campaigns on AI existential threat by specific AI groups (such as OpenAI, Anthropic, DeepMind, and Conjecture) might be an at effort at regulative capture and to pump up interest in their items. [164] [165]

In 2023, the CEOs of Google DeepMind, OpenAI and Anthropic, along with other market leaders and scientists, provided a joint declaration asserting that “Mitigating the threat of termination from AI ought to be a worldwide priority together with other societal-scale threats such as pandemics and nuclear war.” [152]

Mass unemployment

Researchers from OpenAI estimated that “80% of the U.S. labor force might have at least 10% of their work jobs affected by the introduction of LLMs, while around 19% of workers might see a minimum of 50% of their jobs impacted”. [166] [167] They think about workplace workers to be the most exposed, for instance mathematicians, accounting professionals or web designers. [167] AGI could have a much better autonomy, capability to make decisions, to user interface with other computer system tools, however likewise to control robotized bodies.

According to Stephen Hawking, the result of automation on the lifestyle will depend upon how the wealth will be rearranged: [142]

Everyone can delight in a life of glamorous leisure if the machine-produced wealth is shared, or many individuals can end up miserably poor if the machine-owners successfully lobby against wealth redistribution. So far, the trend seems to be towards the second alternative, with innovation driving ever-increasing inequality

Elon Musk considers that the automation of society will require federal governments to adopt a universal standard income. [168]

See likewise

Artificial brain – Software and hardware with cognitive abilities comparable to those of the animal or human brain
AI effect
AI safety – Research location on making AI safe and useful
AI positioning – AI conformance to the intended goal
A.I. Rising – 2018 movie directed by Lazar Bodroža
Expert system
Automated machine knowing – Process of automating the application of artificial intelligence
BRAIN Initiative – Collaborative public-private research effort announced by the Obama administration
China Brain Project
Future of Humanity Institute – Defunct Oxford interdisciplinary research study centre
General video game playing – Ability of synthetic intelligence to play different video games
Generative expert system – AI system capable of producing content in reaction to prompts
Human Brain Project – Scientific research task
Intelligence amplification – Use of information innovation to enhance human intelligence (IA).
Machine principles – Moral behaviours of man-made machines.
Moravec’s paradox.
Multi-task knowing – Solving multiple machine finding out tasks at the very same time.
Neural scaling law – Statistical law in machine knowing.
Outline of expert system – Overview of and topical guide to artificial intelligence.
Transhumanism – Philosophical movement.
Synthetic intelligence – Alternate term for or type of expert system.
Transfer learning – Artificial intelligence strategy.
Loebner Prize – Annual AI competition.
Hardware for synthetic intelligence – Hardware specifically designed and optimized for expert system.
Weak artificial intelligence – Form of artificial intelligence.

Notes

^ a b See below for the origin of the term “strong AI“, and see the academic definition of “strong AI” and weak AI in the article Chinese room.
^ AI creator John McCarthy composes: “we can not yet characterize in basic what kinds of computational treatments we desire to call intelligent. ” [26] (For a conversation of some definitions of intelligence utilized by synthetic intelligence researchers, see approach of artificial intelligence.).
^ The Lighthill report specifically slammed AI‘s “grandiose goals” and led the dismantling of AI research in England. [55] In the U.S., DARPA became determined to money only “mission-oriented direct research, instead of standard undirected research study”. [56] [57] ^ As AI founder John McCarthy composes “it would be a fantastic relief to the remainder of the employees in AI if the developers of new basic formalisms would reveal their hopes in a more protected form than has actually in some cases been the case.” [61] ^ In “Mind Children” [122] 1015 cps is used. More just recently, in 1997, [123] Moravec argued for 108 MIPS which would roughly correspond to 1014 cps. Moravec talks in regards to MIPS, not “cps”, which is a non-standard term Kurzweil presented.
^ As defined in a basic AI textbook: “The assertion that machines could potentially act smartly (or, possibly better, act as if they were intelligent) is called the ‘weak AI‘ hypothesis by theorists, and the assertion that devices that do so are in fact thinking (instead of imitating thinking) is called the ‘strong AI‘ hypothesis.” [121] ^ Alan Turing made this point in 1950. [36] References

^ Krishna, Sri (9 February 2023). “What is artificial narrow intelligence (ANI)?”. VentureBeat. Retrieved 1 March 2024. ANI is designed to perform a single job.
^ “OpenAI Charter”. OpenAI. Retrieved 6 April 2023. Our objective is to ensure that synthetic basic intelligence benefits all of humanity.
^ Heath, Alex (18 January 2024). “Mark Zuckerberg’s new objective is creating artificial general intelligence”. The Verge. Retrieved 13 June 2024. Our vision is to construct AI that is better than human-level at all of the human senses.
^ Baum, Seth D. (2020 ). A Study of Artificial General Intelligence Projects for Ethics, Risk, and Policy (PDF) (Report). Global Catastrophic Risk Institute. Retrieved 28 November 2024. 72 AGI R&D tasks were identified as being active in 2020.
^ a b c “AI timelines: What do experts in synthetic intelligence anticipate for the future?”. Our World in Data. Retrieved 6 April 2023.
^ Metz, Cade (15 May 2023). “Some Researchers Say A.I. Is Already Here, Stirring Debate in Tech Circles”. The New York City Times. Retrieved 18 May 2023.
^ “AI pioneer Geoffrey Hinton quits Google and cautions of threat ahead”. The New York Times. 1 May 2023. Retrieved 2 May 2023. It is hard to see how you can prevent the bad actors from using it for bad things.
^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric (2023 ). “Sparks of Artificial General Intelligence: Early explores GPT-4”. arXiv preprint. arXiv:2303.12712. GPT-4 reveals sparks of AGI.
^ Butler, Octavia E. (1993 ). Parable of the Sower. Grand Central Publishing. ISBN 978-0-4466-7550-5. All that you touch you change. All that you change changes you.
^ Vinge, Vernor (1992 ). A Fire Upon the Deep. Tor Books. ISBN 978-0-8125-1528-2. The Singularity is coming.
^ Morozov, Evgeny (30 June 2023). “The True Threat of Expert System”. The New York Times. The genuine danger is not AI itself however the method we deploy it.
^ “Impressed by expert system? Experts say AGI is coming next, and it has ‘existential’ dangers”. ABC News. 23 March 2023. Retrieved 6 April 2023. AGI could present existential risks to mankind.
^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. ISBN 978-0-1996-7811-2. The very first superintelligence will be the last innovation that humanity requires to make.
^ Roose, Kevin (30 May 2023). “A.I. Poses ‘Risk of Extinction,’ Industry Leaders Warn”. The New York City Times. Mitigating the danger of termination from AI need to be an international priority.
^ “Statement on AI Risk”. Center for AI Safety. Retrieved 1 March 2024. AI professionals warn of danger of extinction from AI.
^ Mitchell, Melanie (30 May 2023). “Are AI’s Doomsday Scenarios Worth Taking Seriously?”. The New York City Times. We are far from developing makers that can outthink us in general methods.
^ LeCun, Yann (June 2023). “AGI does not present an existential risk”. Medium. There is no reason to fear AI as an existential hazard.
^ Kurzweil 2005, p. 260.
^ a b Kurzweil, Ray (5 August 2005), “Long Live AI“, Forbes, archived from the initial on 14 August 2005: Kurzweil describes strong AI as “machine intelligence with the complete series of human intelligence.”.
^ “The Age of Expert System: George John at TEDxLondonBusinessSchool 2013”. Archived from the original on 26 February 2014. Retrieved 22 February 2014.
^ Newell & Simon 1976, This is the term they utilize for “human-level” intelligence in the physical sign system hypothesis.
^ “The Open University on Strong and Weak AI“. Archived from the original on 25 September 2009. Retrieved 8 October 2007.
^ “What is synthetic superintelligence (ASI)?|Definition from TechTarget”. Enterprise AI. Retrieved 8 October 2023.
^ “Expert system is changing our world – it is on everyone to ensure that it works out”. Our World in Data. Retrieved 8 October 2023.
^ Dickson, Ben (16 November 2023). “Here is how far we are to achieving AGI, according to DeepMind”. VentureBeat.
^ McCarthy, John (2007a). “Basic Questions”. Stanford University. Archived from the original on 26 October 2007. Retrieved 6 December 2007.
^ This list of smart traits is based on the topics covered by significant AI books, including: Russell & Norvig 2003, Luger & Stubblefield 2004, Poole, Mackworth & Goebel 1998 and Nilsson 1998.
^ Johnson 1987.
^ de Charms, R. (1968 ). Personal causation. New York City: Academic Press.
^ a b Pfeifer, R. and Bongard J. C., How the body shapes the method we think: a new view of intelligence (The MIT Press, 2007). ISBN 0-2621-6239-3.
^ White, R. W. (1959 ). “Motivation reconsidered: The principle of competence”. Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966.
^ White, R. W. (1959 ). “Motivation reevaluated: The principle of competence”. Psychological Review. 66 (5 ): 297-333. doi:10.1037/ h0040934. PMID 13844397. S2CID 37385966.
^ Muehlhauser, Luke (11 August 2013). “What is AGI?”. Machine Intelligence Research Institute. Archived from the original on 25 April 2014. Retrieved 1 May 2014.
^ “What is Artificial General Intelligence (AGI)?|4 Tests For Ensuring Artificial General Intelligence”. Talky Blog. 13 July 2019. Archived from the initial on 17 July 2019. Retrieved 17 July 2019.
^ Kirk-Giannini, Cameron Domenico; Goldstein, Simon (16 October 2023). “AI is closer than ever to passing the Turing test for ‘intelligence’. What happens when it does?”. The Conversation. Retrieved 22 September 2024.
^ a b Turing 1950.
^ Turing, Alan (1952 ). B. Jack Copeland (ed.). Can Automatic Calculating Machines Be Said To Think?. Oxford: Oxford University Press. pp. 487-506. ISBN 978-0-1982-5079-1.
^ “Eugene Goostman is a real boy – the Turing Test states so”. The Guardian. 9 June 2014. ISSN 0261-3077. Retrieved 3 March 2024.
^ “Scientists dispute whether computer system ‘Eugene Goostman’ passed Turing test”. BBC News. 9 June 2014. Retrieved 3 March 2024.
^ Jones, Cameron R.; Bergen, Benjamin K. (9 May 2024). “People can not identify GPT-4 from a human in a Turing test”. arXiv:2405.08007 [cs.HC]
^ Varanasi, Lakshmi (21 March 2023). “AI designs like ChatGPT and GPT-4 are acing everything from the bar test to AP Biology. Here’s a list of tough exams both AI variations have actually passed”. Business Insider. Retrieved 30 May 2023.
^ Naysmith, Caleb (7 February 2023). “6 Jobs Expert System Is Already Replacing and How Investors Can Take Advantage Of It”. Retrieved 30 May 2023.
^ Turk, Victoria (28 January 2015). “The Plan to Replace the Turing Test with a ‘Turing Olympics'”. Vice. Retrieved 3 March 2024.
^ Gopani, Avi (25 May 2022). “Turing Test is undependable. The Winograd Schema is outdated. Coffee is the answer”. Analytics India Magazine. Retrieved 3 March 2024.
^ Bhaimiya, Sawdah (20 June 2023). “DeepMind’s co-founder recommended checking an AI chatbot’s capability to turn $100,000 into $1 million to determine human-like intelligence”. Business Insider. Retrieved 3 March 2024.
^ Suleyman, Mustafa (14 July 2023). “Mustafa Suleyman: My new Turing test would see if AI can make $1 million”. MIT Technology Review. Retrieved 3 March 2024.
^ Shapiro, Stuart C. (1992 ). “Artificial Intelligence” (PDF). In Stuart C. Shapiro (ed.). Encyclopedia of Artificial Intelligence (Second ed.). New York City: John Wiley. pp. 54-57. Archived (PDF) from the initial on 1 February 2016. (Section 4 is on “AI-Complete Tasks”.).
^ Yampolskiy, Roman V. (2012 ). Xin-She Yang (ed.). “Turing Test as a Defining Feature of AI-Completeness” (PDF). Expert System, Evolutionary Computation and Metaheuristics (AIECM): 3-17. Archived (PDF) from the original on 22 May 2013.
^ “AI Index: State of AI in 13 Charts”. Stanford University Human-Centered Expert System. 15 April 2024. Retrieved 27 May 2024.
^ Crevier 1993, pp. 48-50.
^ Kaplan, Andreas (2022 ). “Artificial Intelligence, Business and Civilization – Our Fate Made in Machines”. Archived from the original on 6 May 2022. Retrieved 12 March 2022.
^ Simon 1965, p. 96 estimated in Crevier 1993, p. 109.
^ “Scientist on the Set: An Interview with Marvin Minsky”. Archived from the original on 16 July 2012. Retrieved 5 April 2008.
^ Marvin Minsky to Darrach (1970 ), priced quote in Crevier (1993, p. 109).
^ Lighthill 1973; Howe 1994.
^ a b NRC 1999, “Shift to Applied Research Increases Investment”.
^ Crevier 1993, pp. 115-117; Russell & Norvig 2003, pp. 21-22.
^ Crevier 1993, p. 211, Russell & Norvig 2003, p. 24 and see likewise Feigenbaum & McCorduck 1983.
^ Crevier 1993, pp. 161-162, 197-203, 240; Russell & Norvig 2003, p. 25.
^ Crevier 1993, pp. 209-212.
^ McCarthy, John (2000 ). “Reply to Lighthill”. Stanford University. Archived from the original on 30 September 2008. Retrieved 29 September 2007.
^ Markoff, John (14 October 2005). “Behind Artificial Intelligence, a Squadron of Bright Real People”. The New York City Times. Archived from the initial on 2 February 2023. Retrieved 18 February 2017. At its low point, some computer system researchers and software application engineers prevented the term synthetic intelligence for fear of being considered as wild-eyed dreamers.
^ Russell & Norvig 2003, pp. 25-26
^ “Trends in the Emerging Tech Hype Cycle”. Gartner Reports. Archived from the initial on 22 May 2019. Retrieved 7 May 2019.
^ a b Moravec 1988, p. 20
^ Harnad, S. (1990 ). “The Symbol Grounding Problem”. Physica D. 42 (1-3): 335-346. arXiv: cs/9906002. Bibcode:1990 PhyD … 42..335 H. doi:10.1016/ 0167-2789( 90 )90087-6. S2CID 3204300.
^ Gubrud 1997
^ Hutter, Marcus (2005 ). Universal Expert System: Sequential Decisions Based on Algorithmic Probability. Texts in Theoretical Computer Science an EATCS Series. Springer. doi:10.1007/ b138233. ISBN 978-3-5402-6877-2. S2CID 33352850. Archived from the original on 19 July 2022. Retrieved 19 July 2022.
^ Legg, Shane (2008 ). Machine Super Intelligence (PDF) (Thesis). University of Lugano. Archived (PDF) from the original on 15 June 2022. Retrieved 19 July 2022.
^ Goertzel, Ben (2014 ). Artificial General Intelligence. Lecture Notes in Computer Science. Vol. 8598. Journal of Artificial General Intelligence. doi:10.1007/ 978-3-319-09274-4. ISBN 978-3-3190-9273-7. S2CID 8387410.
^ “Who created the term “AGI”?”. goertzel.org. Archived from the initial on 28 December 2018. Retrieved 28 December 2018., via Life 3.0: ‘The term “AGI” was popularized by … Shane Legg, Mark Gubrud and Ben Goertzel’
^ Wang & Goertzel 2007
^ “First International Summer School in Artificial General Intelligence, Main summer school: June 22 – July 3, 2009, OpenCog Lab: July 6-9, 2009”. Archived from the original on 28 September 2020. Retrieved 11 May 2020.
^ “Избираеми дисциплини 2009/2010 – пролетен триместър” [Elective courses 2009/2010 – spring trimester] Факултет по математика и информатика [Faculty of Mathematics and Informatics] (in Bulgarian). Archived from the original on 26 July 2020. Retrieved 11 May 2020.
^ “Избираеми дисциплини 2010/2011 – зимен триместър” [Elective courses 2010/2011 – winter trimester] Факултет по математика и информатика [Faculty of Mathematics and Informatics] (in Bulgarian). Archived from the initial on 26 July 2020. Retrieved 11 May 2020.
^ Shevlin, Henry; Vold, Karina; Crosby, Matthew; Halina, Marta (4 October 2019). “The limits of device intelligence: Despite progress in device intelligence, synthetic general intelligence is still a major obstacle”. EMBO Reports. 20 (10 ): e49177. doi:10.15252/ embr.201949177. ISSN 1469-221X. PMC 6776890. PMID 31531926.
^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (27 March 2023). “Sparks of Artificial General Intelligence: Early try outs GPT-4”. arXiv:2303.12712 [cs.CL]
^ “Microsoft Researchers Claim GPT-4 Is Showing “Sparks” of AGI”. Futurism. 23 March 2023. Retrieved 13 December 2023.
^ Allen, Paul; Greaves, Mark (12 October 2011). “The Singularity Isn’t Near”. MIT Technology Review. Retrieved 17 September 2014.
^ Winfield, Alan. “Artificial intelligence will not develop into a Frankenstein’s beast”. The Guardian. Archived from the initial on 17 September 2014. Retrieved 17 September 2014.
^ Deane, George (2022 ). “Machines That Feel and Think: The Role of Affective Feelings and Mental Action in (Artificial) General Intelligence”. Artificial Life. 28 (3 ): 289-309. doi:10.1162/ artl_a_00368. ISSN 1064-5462. PMID 35881678. S2CID 251069071.
^ a b c Clocksin 2003.
^ Fjelland, Ragnar (17 June 2020). “Why general artificial intelligence will not be recognized”. Humanities and Social Sciences Communications. 7 (1 ): 1-9. doi:10.1057/ s41599-020-0494-4. hdl:11250/ 2726984. ISSN 2662-9992. S2CID 219710554.
^ McCarthy 2007b.
^ Khatchadourian, Raffi (23 November 2015). “The Doomsday Invention: Will synthetic intelligence bring us paradise or damage?”. The New Yorker. Archived from the initial on 28 January 2016. Retrieved 7 February 2016.
^ Müller, V. C., & Bostrom, N. (2016 ). Future development in expert system: A study of professional opinion. In Fundamental issues of synthetic intelligence (pp. 555-572). Springer, Cham.
^ Armstrong, Stuart, and Kaj Sotala. 2012. “How We’re Predicting AI-or Failing To.” In Beyond AI: Artificial Dreams, edited by Jan Romportl, Pavel Ircing, Eva Žáčková, Michal Polák and Radek Schuster, 52-75. Plzeň: University of West Bohemia
^ “Microsoft Now Claims GPT-4 Shows ‘Sparks’ of General Intelligence”. 24 March 2023.
^ Shimek, Cary (6 July 2023). “AI Outperforms Humans in Creativity Test”. Neuroscience News. Retrieved 20 October 2023.
^ Guzik, Erik E.; Byrge, Christian; Gilde, Christian (1 December 2023). “The creativity of makers: AI takes the Torrance Test”. Journal of Creativity. 33 (3 ): 100065. doi:10.1016/ j.yjoc.2023.100065. ISSN 2713-3745. S2CID 261087185.
^ Arcas, Blaise Agüera y (10 October 2023). “Artificial General Intelligence Is Already Here”. Noema.
^ Zia, Tehseen (8 January 2024). “Unveiling of Large Multimodal Models: Shaping the Landscape of Language Models in 2024”. Unite.ai. Retrieved 26 May 2024.
^ “Introducing OpenAI o1-preview”. OpenAI. 12 September 2024.
^ Knight, Will. “OpenAI Announces a Brand-new AI Model, Code-Named Strawberry, forum.altaycoins.com That Solves Difficult Problems Step by Step”. Wired. ISSN 1059-1028. Retrieved 17 September 2024.
^ “OpenAI Employee Claims AGI Has Been Achieved”. Orbital Today. 13 December 2024. Retrieved 27 December 2024.
^ “AI Index: State of AI in 13 Charts”. hai.stanford.edu. 15 April 2024. Retrieved 7 June 2024.
^ “Next-Gen AI: OpenAI and Meta’s Leap Towards Reasoning Machines”. Unite.ai. 19 April 2024. Retrieved 7 June 2024.
^ James, Alex P. (2022 ). “The Why, What, and How of Artificial General Intelligence Chip Development”. IEEE Transactions on Cognitive and Developmental Systems. 14 (2 ): 333-347. arXiv:2012.06338. doi:10.1109/ TCDS.2021.3069871. ISSN 2379-8920. S2CID 228376556. Archived from the original on 28 August 2022. Retrieved 28 August 2022.
^ Pei, Jing; Deng, Lei; Song, Sen; Zhao, Mingguo; Zhang, Youhui; Wu, Shuang; Wang, Guanrui; Zou, Zhe; Wu, Zhenzhi; He, Wei; Chen, Feng; Deng, Ning; Wu, Si; Wang, Yu; Wu, Yujie (2019 ). “Towards artificial general intelligence with hybrid Tianjic chip architecture”. Nature. 572 (7767 ): 106-111. Bibcode:2019 Natur.572..106 P. doi:10.1038/ s41586-019-1424-8. ISSN 1476-4687. PMID 31367028. S2CID 199056116. Archived from the original on 29 August 2022. Retrieved 29 August 2022.
^ Pandey, Mohit; Fernandez, Michael; Gentile, Francesco; Isayev, Olexandr; Tropsha, Alexander; Stern, Abraham C.; Cherkasov, Artem (March 2022). “The transformational function of GPU computing and deep knowing in drug discovery”. Nature Machine Intelligence. 4 (3 ): 211-221. doi:10.1038/ s42256-022-00463-x. ISSN 2522-5839. S2CID 252081559.
^ Goertzel & Pennachin 2006.
^ a b c (Kurzweil 2005, p. 260).
^ a b c Goertzel 2007.
^ Grace, Katja (2016 ). “Error in Armstrong and Sotala 2012”. AI Impacts (blog). Archived from the initial on 4 December 2020. Retrieved 24 August 2020.
^ a b Butz, Martin V. (1 March 2021). “Towards Strong AI“. KI – Künstliche Intelligenz. 35 (1 ): 91-101. doi:10.1007/ s13218-021-00705-x. ISSN 1610-1987. S2CID 256065190.
^ Liu, Feng; Shi, Yong; Liu, Ying (2017 ). “Intelligence Quotient and Intelligence Grade of Artificial Intelligence”. Annals of Data Science. 4 (2 ): 179-191. arXiv:1709.10242. doi:10.1007/ s40745-017-0109-0. S2CID 37900130.
^ Brien, Jörn (5 October 2017). “Google-KI doppelt so schlau wie Siri” [Google AI is twice as smart as Siri – but a six-year-old beats both] (in German). Archived from the initial on 3 January 2019. Retrieved 2 January 2019.
^ Grossman, Gary (3 September 2020). “We’re getting in the AI golden zone in between narrow and basic AI“. VentureBeat. Archived from the original on 4 September 2020. Retrieved 5 September 2020. Certainly, too, there are those who declare we are currently seeing an early example of an AGI system in the recently revealed GPT-3 natural language processing (NLP) neural network. … So is GPT-3 the very first example of an AGI system? This is arguable, however the consensus is that it is not AGI. … If absolutely nothing else, GPT-3 informs us there is a middle ground in between narrow and general AI.
^ Quach, Katyanna. “A developer built an AI chatbot utilizing GPT-3 that assisted a guy speak once again to his late fiancée. OpenAI shut it down”. The Register. Archived from the original on 16 October 2021. Retrieved 16 October 2021.
^ Wiggers, Kyle (13 May 2022), “DeepMind’s new AI can carry out over 600 tasks, from playing games to managing robotics”, TechCrunch, archived from the original on 16 June 2022, recovered 12 June 2022.
^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (22 March 2023). “Sparks of Artificial General Intelligence: Early explores GPT-4”. arXiv:2303.12712 [cs.CL]
^ Metz, Cade (1 May 2023). “‘ The Godfather of A.I.’ Leaves Google and Warns of Danger Ahead”. The New York Times. ISSN 0362-4331. Retrieved 7 June 2023.
^ Bove, Tristan. “A.I. might rival human intelligence in ‘simply a couple of years,’ states CEO of Google’s main A.I. research study laboratory”. Fortune. Retrieved 4 September 2024.
^ Nellis, Stephen (2 March 2024). “Nvidia CEO states AI might pass human tests in five years”. Reuters. ^ Aschenbrenner, Leopold. “SITUATIONAL AWARENESS, The Decade Ahead”.
^ Sullivan, Mark (18 October 2023). “Why everybody seems to disagree on how to define Artificial General Intelligence”. Fast Company.
^ Nosta, John (5 January 2024). “The Accelerating Path to Artificial General Intelligence”. Psychology Today. Retrieved 30 March 2024.
^ Hickey, Alex. “Whole Brain Emulation: A Huge Step for Neuroscience”. Tech Brew. Retrieved 8 November 2023.
^ Sandberg & Boström 2008.
^ Drachman 2005.
^ a b Russell & Norvig 2003.
^ Moravec 1988, p. 61.
^ Moravec 1998.
^ Holmgaard Mersh, Amalie (15 September 2023). “Decade-long European research study task maps the human brain”. euractiv.
^ Swaminathan, Nikhil (January-February 2011). “Glia-the other brain cells”. Discover. Archived from the original on 8 February 2014. Retrieved 24 January 2014.
^ de Vega, Glenberg & Graesser 2008. A large range of views in existing research study, all of which need grounding to some degree
^ Thornton, Angela (26 June 2023). “How submitting our minds to a computer might end up being possible”. The Conversation. Retrieved 8 November 2023.
^ Searle 1980
^ For instance: Russell & Norvig 2003,
Oxford University Press Dictionary of Psychology Archived 3 December 2007 at the Wayback Machine (priced quote in” Encyclopedia.com”),.
MIT Encyclopedia of Cognitive Science Archived 19 July 2008 at the Wayback Machine (quoted in “AITopics”),.
Will Biological Computers Enable Artificially Intelligent Machines to Become Persons? Archived 13 May 2008 at the Wayback Machine Anthony Tongen.

^ a b c Russell & Norvig 2003, p. 947.
^ though see Explainable synthetic intelligence for curiosity by the field about why a program behaves the method it does.
^ Chalmers, David J. (9 August 2023). “Could a Large Language Model Be Conscious?”. Boston Review.
^ Seth, Anil. “Consciousness”. New Scientist. Retrieved 5 September 2024.
^ Nagel 1974.
^ “The Google engineer who believes the company’s AI has actually come to life”. The Washington Post. 11 June 2022. Retrieved 12 June 2023.
^ Kateman, Brian (24 July 2023). “AI Should Be Terrified of Humans”. TIME. Retrieved 5 September 2024.
^ Nosta, John (18 December 2023). “Should Artificial Intelligence Have Rights?”. Psychology Today. Retrieved 5 September 2024.
^ Akst, Daniel (10 April 2023). “Should Robots With Artificial Intelligence Have Moral or Legal Rights?”. The Wall Street Journal.
^ “Artificial General Intelligence – Do [es] the expense surpass advantages?”. 23 August 2021. Retrieved 7 June 2023.
^ “How we can Take advantage of Advancing Artificial General Intelligence (AGI) – Unite.AI“. www.unite.ai. 7 April 2020. Retrieved 7 June 2023.
^ a b c Talty, Jules; Julien, Stephan. “What Will Our Society Look Like When Artificial Intelligence Is Everywhere?”. Smithsonian Magazine. Retrieved 7 June 2023.
^ a b Stevenson, Matt (8 October 2015). “Answers to Stephen Hawking’s AMA are Here!”. Wired. ISSN 1059-1028. Retrieved 8 June 2023.
^ a b Bostrom, Nick (2017 ). ” § Preferred order of arrival”. Superintelligence: paths, dangers, techniques (Reprinted with corrections 2017 ed.). Oxford, UK; New York City, New York, USA: Oxford University Press. ISBN 978-0-1996-7811-2.
^ Piper, Kelsey (19 November 2018). “How technological development is making it likelier than ever that people will destroy ourselves”. Vox. Retrieved 8 June 2023.
^ Doherty, Ben (17 May 2018). “Climate alter an ‘existential security danger’ to Australia, Senate questions states”. The Guardian. ISSN 0261-3077. Retrieved 16 July 2023.
^ MacAskill, William (2022 ). What we owe the future. New York City, NY: Basic Books. ISBN 978-1-5416-1862-6.
^ a b Ord, Toby (2020 ). “Chapter 5: Future Risks, Unaligned Expert System”. The Precipice: Existential Risk and the Future of Humanity. Bloomsbury Publishing. ISBN 978-1-5266-0021-9.
^ Al-Sibai, Noor (13 February 2022). “OpenAI Chief Scientist Says Advanced AI May Already Be Conscious”. Futurism. Retrieved 24 December 2023.
^ Samuelsson, Paul Conrad (2019 ). “Artificial Consciousness: Our Greatest Ethical Challenge”. Philosophy Now. Retrieved 23 December 2023.
^ Kateman, Brian (24 July 2023). “AI Should Be Terrified of Humans”. TIME. Retrieved 23 December 2023.
^ Roose, Kevin (30 May 2023). “A.I. Poses ‘Risk of Extinction,’ Industry Leaders Warn”. The New York City Times. ISSN 0362-4331. Retrieved 24 December 2023.
^ a b “Statement on AI Risk”. Center for AI Safety. 30 May 2023. Retrieved 8 June 2023.
^ “Stephen Hawking: ‘Transcendence looks at the ramifications of expert system – however are we taking AI seriously enough?'”. The Independent (UK). Archived from the original on 25 September 2015. Retrieved 3 December 2014.
^ Herger, Mario. “The Gorilla Problem – Enterprise Garage”. Retrieved 7 June 2023.
^ “The interesting Facebook argument in between Yann LeCun, Stuart Russel and Yoshua Bengio about the threats of strong AI“. The fascinating Facebook dispute between Yann LeCun, Stuart Russel and Yoshua Bengio about the dangers of strong AI (in French). Retrieved 8 June 2023.
^ “Will Expert System Doom The Human Race Within The Next 100 Years?”. HuffPost. 22 August 2014. Retrieved 8 June 2023.
^ Sotala, Kaj; Yampolskiy, Roman V. (19 December 2014). “Responses to devastating AGI threat: a survey”. Physica Scripta. 90 (1 ): 018001. doi:10.1088/ 0031-8949/90/ 1/018001. ISSN 0031-8949.
^ Bostrom, Nick (2014 ). Superintelligence: Paths, Dangers, Strategies (First ed.). Oxford University Press. ISBN 978-0-1996-7811-2.
^ Chow, Andrew R.; Perrigo, Billy (16 February 2023). “The AI Arms Race Is On. Start Worrying”. TIME. Retrieved 24 December 2023.
^ Tetlow, Gemma (12 January 2017). “AI arms race risks spiralling out of control, report warns”. Financial Times. Archived from the original on 11 April 2022. Retrieved 24 December 2023.
^ Milmo, Dan; Stacey, Kiran (25 September 2023). “Experts disagree over risk presented but synthetic intelligence can not be overlooked”. The Guardian. ISSN 0261-3077. Retrieved 24 December 2023.
^ “Humanity, Security & AI, Oh My! (with Ian Bremmer & Shuman Ghosemajumder)”. CAFE. 20 July 2023. Retrieved 15 September 2023.
^ Hamblin, James (9 May 2014). “But What Would completion of Humanity Mean for Me?”. The Atlantic. Archived from the initial on 4 June 2014. Retrieved 12 December 2015.
^ Titcomb, James (30 October 2023). “Big Tech is stiring worries over AI, alert researchers”. The Telegraph. Retrieved 7 December 2023.
^ Davidson, John (30 October 2023). “Google Brain creator says huge tech is lying about AI termination risk”. Australian Financial Review. Archived from the original on 7 December 2023. Retrieved 7 December 2023.
^ Eloundou, Tyna; Manning, Sam; Mishkin, Pamela; Rock, Daniel (17 March 2023). “GPTs are GPTs: An early look at the labor market impact capacity of large language models”. OpenAI. Retrieved 7 June 2023.
^ a b Hurst, Luke (23 March 2023). “OpenAI states 80% of workers could see their jobs affected by AI. These are the tasks most impacted”. euronews. Retrieved 8 June 2023.
^ Sheffey, Ayelet (20 August 2021). “Elon Musk states we need universal basic income due to the fact that ‘in the future, physical work will be a choice'”. Business Insider. Archived from the original on 9 July 2023. Retrieved 8 June 2023.
Sources

UNESCO Science Report: the Race Against Time for Smarter Development. Paris: UNESCO. 11 June 2021. ISBN 978-9-2310-0450-6. Archived from the initial on 18 June 2022. Retrieved 22 September 2021.
Chalmers, David (1996 ), The Conscious Mind, Oxford University Press.
Clocksin, William (August 2003), “Artificial intelligence and the future”, Philosophical Transactions of the Royal Society A, vol. 361, no. 1809, pp. 1721-1748, Bibcode:2003 RSPTA.361.1721 C, doi:10.1098/ rsta.2003.1232, PMID 12952683, S2CID 31032007.
Crevier, Daniel (1993 ). AI: The Tumultuous Search for Artificial Intelligence. New York, NY: BasicBooks. ISBN 0-465-02997-3.
Darrach, Brad (20 November 1970), “Meet Shakey, the First Electronic Person”, Life Magazine, pp. 58-68.
Drachman, D. (2005 ), “Do we have brain to spare?”, Neurology, 64 (12 ): 2004-2005, doi:10.1212/ 01. WNL.0000166914.38327. BB, PMID 15985565, S2CID 38482114.
Feigenbaum, Edward A.; McCorduck, Pamela (1983 ), The Fifth Generation: Artificial Intelligence and Japan’s Computer Challenge to the World, Michael Joseph, ISBN 978-0-7181-2401-4.
Goertzel, Ben; Pennachin, Cassio, eds. (2006 ), Artificial General Intelligence (PDF), Springer, ISBN 978-3-5402-3733-4, archived from the initial (PDF) on 20 March 2013.
Goertzel, Ben (December 2007), “Human-level artificial general intelligence and the possibility of a technological singularity: a reaction to Ray Kurzweil’s The Singularity Is Near, and McDermott’s critique of Kurzweil”, Expert system, vol. 171, no. 18, Special Review Issue, pp. 1161-1173, doi:10.1016/ j.artint.2007.10.011, archived from the initial on 7 January 2016, retrieved 1 April 2009.
Gubrud, Mark (November 1997), “Nanotechnology and International Security”, Fifth Foresight Conference on Molecular Nanotechnology, archived from the original on 29 May 2011, retrieved 7 May 2011.
Howe, J. (November 1994), Artificial Intelligence at Edinburgh University: a Viewpoint, archived from the original on 17 August 2007, recovered 30 August 2007.
Johnson, Mark (1987 ), The body in the mind, Chicago, ISBN 978-0-2264-0317-5.
Kurzweil, Ray (2005 ), The Singularity is Near, Viking Press.
Lighthill, Professor Sir James (1973 ), “Artificial Intelligence: A General Survey”, Artificial Intelligence: a paper symposium, Science Research Council.
Luger, George; Stubblefield, William (2004 ), Expert System: Structures and Strategies for Complex Problem Solving (fifth ed.), The Benjamin/Cummings Publishing Company, Inc., p. 720, ISBN 978-0-8053-4780-7.
McCarthy, John (2007b). What is Artificial Intelligence?. Stanford University. The ultimate effort is to make computer programs that can resolve issues and accomplish objectives on the planet in addition to human beings.
Moravec, Hans (1988 ), Mind Children, Harvard University Press
Moravec, Hans (1998 ), “When will hardware match the human brain?”, Journal of Evolution and Technology, vol. 1, archived from the original on 15 June 2006, obtained 23 June 2006
Nagel (1974 ), “What Is it Like to Be a Bat” (PDF), Philosophical Review, 83 (4 ): 435-50, doi:10.2307/ 2183914, JSTOR 2183914, archived (PDF) from the initial on 16 October 2011, retrieved 7 November 2009
Newell, Allen; Simon, H. A. (1976 ). “Computer Technology as Empirical Inquiry: Symbols and Search”. Communications of the ACM. 19 (3 ): 113-126. doi:10.1145/ 360018.360022.
Nilsson, Nils (1998 ), Artificial Intelligence: A New Synthesis, Morgan Kaufmann Publishers, ISBN 978-1-5586-0467-4
NRC (1999 ), “Developments in Expert System”, Funding a Transformation: Government Support for Computing Research, National Academy Press, archived from the initial on 12 January 2008, retrieved 29 September 2007
Poole, David; Mackworth, Alan; Goebel, Randy (1998 ), Computational Intelligence: A Sensible Approach, New York: Oxford University Press, archived from the original on 25 July 2009, retrieved 6 December 2007
Russell, Stuart J.; Norvig, Peter (2003 ), Expert System: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2
Sandberg, Anders; Boström, Nick (2008 ), Whole Brain Emulation: A Roadmap (PDF), Technical Report # 2008-3, Future of Humanity Institute, Oxford University, archived (PDF) from the original on 25 March 2020, retrieved 5 April 2009
Searle, John (1980 ), “Minds, Brains and Programs” (PDF), Behavioral and Brain Sciences, 3 (3 ): 417-457, doi:10.1017/ S0140525X00005756, S2CID 55303721, archived (PDF) from the original on 17 March 2019, obtained 3 September 2020
Simon, H. A. (1965 ), The Shape of Automation for Men and Management, New York City: Harper & Row
Turing, Alan (October 1950). “Computing Machinery and Intelligence”. Mind. 59 (236 ): 433-460. doi:10.1093/ mind/LIX.236.433. ISSN 1460-2113. JSTOR 2251299. S2CID 14636783.

de Vega, Manuel; Glenberg, Arthur; Graesser, Arthur, eds. (2008 ), Symbols and Embodiment: Debates on meaning and cognition, Oxford University Press, ISBN 978-0-1992-1727-4
Wang, Pei; Goertzel, Ben (2007 ). “Introduction: Aspects of Artificial General Intelligence”. Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the AGI Workshop 2006. IOS Press. pp. 1-16. ISBN 978-1-5860-3758-1. Archived from the original on 18 February 2021. Retrieved 13 December 2020 – by means of ResearchGate.

Further reading

Aleksander, Igor (1996 ), Impossible Minds, World Scientific Publishing Company, ISBN 978-1-8609-4036-1
Azevedo FA, Carvalho LR, Grinberg LT, Farfel J, et al. (April 2009), “Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain”, The Journal of Comparative Neurology, 513 (5 ): 532-541, doi:10.1002/ cne.21974, PMID 19226510, S2CID 5200449, archived from the original on 18 February 2021, obtained 4 September 2013 – by means of ResearchGate
Berglas, Anthony (January 2012) [2008], Artificial Intelligence Will Kill Our Grandchildren (Singularity), archived from the initial on 23 July 2014, recovered 31 August 2012
Cukier, Kenneth, “Ready for Robots? How to Consider the Future of AI“, Foreign Affairs, vol. 98, no. 4 (July/August 2019), pp. 192-98. George Dyson, historian of computing, writes (in what may be called “Dyson’s Law”) that “Any system easy adequate to be easy to understand utahsyardsale.com will not be made complex enough to behave smartly, while any system made complex enough to behave intelligently will be too complicated to comprehend.” (p. 197.) Computer scientist Alex Pentland writes: “Current AI machine-learning algorithms are, at their core, dead basic stupid. They work, however they work by strength.” (p. 198.).
Gelernter, David, Dream-logic, the Internet and Artificial Thought, Edge, archived from the initial on 26 July 2010, recovered 25 July 2010.
Gleick, James, “The Fate of Free Choice” (review of Kevin J. Mitchell, Free Agents: How Evolution Gave Us Free Will, Princeton University Press, 2023, 333 pp.), The New York City Review of Books, vol. LXXI, no. 1 (18 January 2024), pp. 27-28, 30. “Agency is what distinguishes us from devices. For biological creatures, factor and purpose originate from acting on the planet and experiencing the consequences. Expert systems – disembodied, bytes-the-dust.com complete strangers to blood, sweat, and tears – have no event for that.” (p. 30.).
Halal, William E. “TechCast Article Series: The Automation of Thought” (PDF). Archived from the initial (PDF) on 6 June 2013.
– Halpern, Sue, “The Coming Tech Autocracy” (evaluation of Verity Harding, AI Needs You: How We Can Change AI‘s Future and Save Our Own, Princeton University Press, 274 pp.; Gary Marcus, Taming Silicon Valley: How We Can Ensure That AI Works for Us, MIT Press, 235 pp.; Daniela Rus and Gregory Mone, The Mind’s Mirror: Risk and Reward in the Age of AI, Norton, 280 pp.; Madhumita Murgia, Code Dependent: Living in the Shadow of AI, Henry Holt, 311 pp.), The New York Review of Books, vol. LXXI, no. 17 (7 November 2024), pp. 44-46. “‘ We can’t reasonably anticipate that those who hope to get abundant from AI are going to have the interests of the rest people close at heart,’ … composes [Gary Marcus] ‘We can’t depend on governments driven by project finance contributions [from tech companies] to push back.’ … Marcus details the demands that people should make of their federal governments and the tech companies. They include transparency on how AI systems work; settlement for people if their information [are] used to train LLMs (large language design) s and the right to grant this use; and the ability to hold tech business responsible for the damages they trigger by removing Section 230, imposing money penalites, and passing more stringent item liability laws … Marcus also recommends … that a brand-new, AI-specific federal firm, comparable to the FDA, the FCC, or the FTC, may provide the most robust oversight … [T] he Fordham law professor Chinmayi Sharma … suggests … develop [ing] an expert licensing regime for engineers that would work in a comparable way to medical licenses, malpractice suits, and the Hippocratic oath in medicine. ‘What if, like physicians,’ she asks …, ‘AI engineers likewise swore to do no harm?'” (p. 46.).
Holte, R. C.; Choueiry, B. Y. (2003 ), “Abstraction and reformulation in expert system”, Philosophical Transactions of the Royal Society B, vol. 358, no. 1435, pp. 1197-1204, doi:10.1098/ rstb.2003.1317, PMC 1693218, PMID 12903653.
Hughes-Castleberry, Kenna, “A Murder Mystery Puzzle: The literary puzzle Cain’s Jawbone, which has actually baffled people for decades, exposes the restrictions of natural-language-processing algorithms”, Scientific American, vol. 329, no. 4 (November 2023), pp. 81-82. “This murder mystery competitors has exposed that although NLP (natural-language processing) models can unbelievable feats, their capabilities are quite restricted by the amount of context they get. This […] might cause [troubles] for scientists who intend to utilize them to do things such as evaluate ancient languages. In many cases, there are few historical records on long-gone civilizations to serve as training data for such a purpose.” (p. 82.).
Immerwahr, Daniel, “Your Lying Eyes: People now utilize A.I. to produce phony videos equivalent from genuine ones. Just how much does it matter?”, The New Yorker, 20 November 2023, pp. 54-59. “If by ‘deepfakes’ we suggest reasonable videos produced utilizing synthetic intelligence that actually trick people, then they barely exist. The fakes aren’t deep, and the deeps aren’t phony. […] A.I.-generated videos are not, in basic, operating in our media as counterfeited evidence. Their role better resembles that of animations, specifically smutty ones.” (p. 59.).
– Leffer, Lauren, “The Risks of Trusting AI: We need to avoid humanizing machine-learning models utilized in clinical research study”, Scientific American, vol. 330, no. 6 (June 2024), pp. 80-81.
Lepore, Jill, “The Chit-Chatbot: yewiki.org Is talking with a device a discussion?”, The New Yorker, 7 October 2024, pp. 12-16.
Marcus, Gary, “Artificial Confidence: Even the newest, buzziest systems of artificial basic intelligence are stymmied by the usual issues”, Scientific American, vol. 327, no. 4 (October 2022), pp. 42-45.
McCarthy, John (October 2007), “From here to human-level AI“, Expert System, 171 (18 ): 1174-1182, doi:10.1016/ j.artint.2007.10.009.
McCorduck, Pamela (2004 ), Machines Who Think (second ed.), Natick, Massachusetts: A. K. Peters, ISBN 1-5688-1205-1.
Moravec, Hans (1976 ), The Role of Raw Power in Intelligence, archived from the initial on 3 March 2016, obtained 29 September 2007.
Newell, Allen; Simon, H. A. (1963 ), “GPS: A Program that Simulates Human Thought”, in Feigenbaum, E. A.; Feldman, J. (eds.), Computers and Thought, New York: McGraw-Hill.
Omohundro, Steve (2008 ), The Nature of Self-Improving Expert system, presented and dispersed at the 2007 Singularity Summit, San Francisco, California.
Press, Eyal, “In Front of Their Faces: Does facial-recognition innovation lead authorities to overlook inconsistent proof?”, The New Yorker, 20 November 2023, pp. 20-26.
Roivainen, Eka, “AI‘s IQ: ChatGPT aced a [standard intelligence] test however showed that intelligence can not be determined by IQ alone”, Scientific American, vol. 329, no. 1 (July/August 2023), p. 7. “Despite its high IQ, ChatGPT stops working at tasks that require real humanlike reasoning or an understanding of the physical and social world … ChatGPT appeared not able to factor realistically and tried to depend on its vast database of … truths derived from online texts. ”
– Scharre, Paul, “Killer Apps: historydb.date The Real Dangers of an AI Arms Race”, Foreign Affairs, vol. 98, no. 3 (May/June 2019), pp. 135-44. “Today’s AI innovations are powerful however unreliable. Rules-based systems can not handle scenarios their programmers did not expect. Learning systems are restricted by the information on which they were trained. AI failures have currently led to disaster. Advanced auto-pilot features in vehicles, although they carry out well in some scenarios, have driven vehicles without warning into trucks, concrete barriers, and parked automobiles. In the wrong circumstance, AI systems go from supersmart to superdumb in an instant. When an enemy is trying to control and hack an AI system, the dangers are even higher.” (p. 140.).
Sutherland, J. G. (1990 ), “Holographic Model of Memory, Learning, and Expression”, International Journal of Neural Systems, vol. 1-3, pp. 256-267.
– Vincent, James, “Horny Robot Baby Voice: James Vincent on AI chatbots”, London Review of Books, vol. 46, no. 19 (10 October 2024), pp. 29-32.” [AI chatbot] programs are made possible by brand-new technologies but depend on the timelelss human tendency to anthropomorphise.” (p. 29.).
Williams, R. W.; Herrup, K.